Research on plant stem complement based on L1-medial skeleton extraction
Authors: Jincen, J., Li, L. and Wang, M.
Journal: Journal of Nanjing Forestry University (Natural Sciences Edition)
Volume: 46
Issue: 1
Pages: 40-50
ISSN: 1000-2006
DOI: 10.12302/j.issn.1000-2006.202110040
Abstract:[Objective] Plant visualization technology is an important part of digital forestry research. This study used thestem missing points to propose a plant stem complement based on L1-medial skeleton extraction to provide technicalsupport for plant visualization. [Method] First, a method to determine the position of the missing part based ontopological connection was utilized. Based on the density of the point cloud, the point cloud was classed by using theunion-find sets. The weight of the edge of the cluster between nodes was calculated based on the probability graph model,and the minimum spanning tree was used to determine the topological connection between clusters; this enabled thedetermination of the position of missing parts. Following this, a search-based method was used to determine the set ofpoints to be fitted. The L1-medial local iterative method was used to extract the stem point cloud skeleton. A searchalgorithm based on nearest neighbor distance was proposed to sort the skeleton point set for the disorder of the pointcloud, to determine points to be fitted for missing parts. To address issues with inaccurate skeleton extraction, aniterative optimization method of stem skeleton based on Gauss kernel weight was proposed. This approach used Gausssmoothing stem direction vector, and the Gauss weighted average to calculate stem radius and the updated stem skeletonpoint set. Finally, a method of point cloud completion of missing parts based on fitting was utilized. The stem radius ofmissing parts was fitted based on the least squares method, and the stem line of missing parts was fitted based on theBezier curve. A method based on point cloud density to adjust the generation of the fitting point cloud was proposed tobetter fit the actual point cloud. [Result] The experimental results show that the method proposed in this paper caneffectively complete the plant point cloud with leaf and stem missing separation; the fitting result was smooth and hascertain practical, physical significance. [Conclusion] To some extent, the research results in this paper make up for thedefects of scanning, building a complete and realistic three-dimensional point cloud model of plants; this may be moreeffectively applied to the three-dimensional visual modeling of plants.
Source: Scopus